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Institution

Uppsala University

EducationUppsala, Sweden
About: Uppsala University is a education organization based out in Uppsala, Sweden. It is known for research contribution in the topics: Population & Gene. The organization has 36485 authors who have published 107509 publications receiving 4220668 citations. The organization is also known as: Uppsala universitet & uu.se.


Papers
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Journal ArticleDOI
TL;DR: By combining genome-wide association data from 8,130 individuals with type 2 diabetes and 38,987 controls of European descent and following up previously unidentified meta-analysis signals, 12 new T2D association signals are identified with combined P < 5 × 10−8.
Abstract: By combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P<5x10(-8). These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A). The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation. We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits.

1,785 citations

Journal ArticleDOI
TL;DR: Hyaluronan’s nature, distribution, functions and turnover are studied in detail in a large sample of animals from around the world.
Abstract: Hyaluronan is a polysaccharide found in all tissues and body fluids of vertebrates as well as in some bacteria. It is a linear polymer of exceptional molecular weight, especially abundant in loose connective tissue. Hyaluronan is synthesized in the cellular plasma membrane. It exists as a pool associated with the cell surface, another bound to other matrix components, and a largely mobile pool. A number of proteins, the hyaladherins, specifically recognize the hyaluronan structure. Interactions of this kind bind hyaluronan with proteoglycans to stabilize the structure of the matrix, and with cell surfaces to modify cell behaviour. Because of the striking physicochemical properties of hyaluronan solutions, various physiological functions have been assigned to it, including lubrication, water homeostasis, filtering effects and regulation of plasma protein distribution. In animals and man, the half-life of hyaluronan in tissues ranges from less than 1 to several days. It is catabolized by receptor-mediated endocytosis and lysosomal degradation either locally or after transport by lymph to lymph nodes which degrade much of it. The remainder enters the general circulation and is removed from blood, with a half-life of 2-5 min, mainly by the endothelial cells of the liver sinuoids.

1,775 citations

Journal ArticleDOI
17 Aug 2000-Nature
TL;DR: Computer simulations are used to investigate the structural information that can be recovered from the scattering of intense femtosecond X-ray pulses by single protein molecules and small assemblies and predict that ultrashort, high-intensity X-rays from free-electron lasers that are currently under development will provide a new approach to structural determinations with X- rays.
Abstract: Sample damage by X-rays and other radiation limits the resolution of structural studies on non-repetitive and non-reproducible structures such as individual biomolecules or cells(1). Cooling can slow sample deterioration, but cannot eliminate damage-induc

1,770 citations

Journal ArticleDOI
Hana Lango Allen1, Karol Estrada2, Guillaume Lettre3, Sonja I. Berndt4  +341 moreInstitutions (90)
14 Oct 2010-Nature
TL;DR: It is shown that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait, and indicates that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.
Abstract: Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

1,768 citations

Journal ArticleDOI
TL;DR: A Bayesian MCMC approach to the analysis of combined data sets was developed and its utility in inferring relationships among gall wasps based on data from morphology and four genes was explored, supporting the utility of morphological data in multigene analyses.
Abstract: The recent development of Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) techniques has facilitated the exploration of parameter-rich evolutionary models. At the same time, stochastic models have become more realistic (and complex) and have been extended to new types of data, such as morphology. Based on this foundation, we developed a Bayesian MCMC approach to the analysis of combined data sets and explored its utility in inferring relationships among gall wasps based on data from morphology and four genes (nuclear and mitochondrial, ribosomal and protein coding). Examined models range in complexity from those recognizing only a morphological and a molecular partition to those having complex substitution models with independent parameters for each gene. Bayesian MCMC analysis deals efficiently with complex models: convergence occurs faster and more predictably for complex models, mixing is adequate for all parameters even under very complex models, and the parameter update cycle is virtually unaffected by model partitioning across sites. Morphology contributed only 5% of the characters in the data set but nevertheless influenced the combined-data tree, supporting the utility of morphological data in multigene analyses. We used Bayesian criteria (Bayes factors) to show that process heterogeneity across data partitions is a significant model component, although not as important as among-site rate variation. More complex evolutionary models are associated with more topological uncertainty and less conflict between morphology and molecules. Bayes factors sometimes favor simpler models over considerably more parameter-rich models, but the best model overall is also the most complex and Bayes factors do not support exclusion of apparently weak parameters from this model. Thus, Bayes factors appear to be useful for selecting among complex models, but it is still unclear whether their use strikes a reasonable balance between model complexity and error in parameter estimates.

1,758 citations


Authors

Showing all 36854 results

NameH-indexPapersCitations
Zhong Lin Wang2452529259003
Lewis C. Cantley196748169037
Darien Wood1602174136596
Kaj Blennow1601845116237
Christopher J. O'Donnell159869126278
Tomas Hökfelt158103395979
Peter G. Schultz15689389716
Frederik Barkhof1541449104982
Deepak L. Bhatt1491973114652
Svante Pääbo14740784489
Jan-Åke Gustafsson147105898804
Hans-Olov Adami14590883473
Hermann Kolanoski145127996152
Kjell Fuxe142147989846
Jan Conrad14182671445
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023240
2022643
20216,080
20205,811
20195,393
20185,067